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china-data-elements

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data-economy
data-governance
china
policy
data-markets
artificial-intelligence
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china-data-elements / code /build.py
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Kukai-7
Add road to 2030: official targets and scenarios
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"""Build the China data elements dataset: data as a factor of production, from policy to market. All tables are curated from official
releases and press reports of the National Data Administration, the Ministry of Finance, CAICT and others; each row carries its source."""
import json, os, pandas as pd
OUT = 'out/data'
os.makedirs(OUT, exist_ok=True)
# ------------------------------------------------------------------ policy, law and institutions (date of adoption, publication or opening)
P = [('2015-04-14', 'Exchange', 'Guiyang Global Big Data Exchange opens', '贵阳大数据交易所挂牌', 'Guiyang', 'First data exchange in China'),
('2015-08-31', 'Policy', 'State Council Action Outline for Promoting Big Data Development', '促进大数据发展行动纲要', 'Beijing', 'First national big-data strategy'),
('2016-11-07', 'Law', 'Cybersecurity Law adopted', '网络安全法', 'Beijing', 'In force 1 June 2017'),
('2019-10-31', 'Policy', 'Fourth Plenum names data a factor of production', '十九届四中全会：数据作为生产要素参与分配', 'Beijing', 'Data joins labour, capital, land, knowledge, technology and management'),
('2020-03-30', 'Policy', 'Opinions on market-based allocation of factors', '关于构建更加完善的要素市场化配置体制机制的意见', 'Beijing', 'Data listed as the fifth factor market'),
('2021-03-31', 'Exchange', 'Beijing International Big Data Exchange opens', '北京国际大数据交易所', 'Beijing', ''),
('2021-06-10', 'Law', 'Data Security Law adopted', '数据安全法', 'Beijing', 'In force 1 September 2021'),
('2021-08-20', 'Law', 'Personal Information Protection Law adopted', '个人信息保护法', 'Beijing', 'In force 1 November 2021'),
('2021-11-25', 'Exchange', 'Shanghai Data Exchange opens', '上海数据交易所', 'Shanghai', ''),
('2022-09-30', 'Exchange', 'Guangzhou Data Exchange opens', '广州数据交易所', 'Guangzhou', ''),
('2022-11-15', 'Exchange', 'Shenzhen Data Exchange opens', '深圳数据交易所', 'Shenzhen', ''),
('2022-12-19', 'Policy', '"Twenty Measures on Data": opinions on the basic data system', '数据二十条', 'Beijing', 'Separates rights to hold, process and operate data'),
('2023-08-21', 'Accounting', 'Interim rules on accounting for enterprise data resources', '企业数据资源相关会计处理暂行规定', 'Beijing', 'Data can be booked as intangible assets or inventory from 1 January 2024'),
('2023-10-25', 'Institution', 'National Data Administration inaugurated', '国家数据局挂牌', 'Beijing', 'Approved in the State Council reform of March 2023'),
('2024-01-04', 'Policy', '"Data Element ×" three-year action plan 2024 to 2026', '"数据要素×"三年行动计划', 'Beijing', '17 departments, 12 sectors'),
('2024-03-22', 'Law', 'Rules on promoting and regulating cross-border data flows', '促进和规范数据跨境流动规定', 'Beijing', 'Cyberspace Administration of China'),
('2024-10-27', 'Competition', 'First "Data Element ×" competition final', '首届"数据要素×"大赛全国总决赛', 'Beijing', 'Over 19,000 teams'),
('2025-11-25', 'Competition', 'Second "Data Element ×" competition final', '2025年"数据要素×"大赛全国总决赛', 'Shanghai', 'Over 23,000 teams'),
('2025-12-30', 'Policy', 'National data work conference: 2026 named the year of releasing data value', '全国数据工作会议："数据要素价值释放年"', 'Beijing', ''),
('2026-10-23', 'Competition', 'Third "Data Element ×" competition final', '2026年"数据要素×"大赛全国总决赛', 'Qingdao', 'Over 29,000 teams; last year of the three-year plan')]
pol = pd.DataFrame(P, columns=['date', 'kind', 'event', 'event_zh', 'city', 'note'])
pol['date'] = pd.to_datetime(pol.date); pol['year'] = pol.date.dt.year
pol['date_precision'] = 'day'
pol.loc[pol.event.str.startswith('First "Data'), 'date_precision'] = 'on or shortly before (reported 27 October 2024)'
pol.to_parquet(f'{OUT}/policy_timeline.parquet', index=False)
# ------------------------------------------------------------------ indicators (long format)
I = [('data_production', 2023, 32.85, 'ZB', 'National data resources survey 2023 (NDA, May 2024)'),
('data_production', 2024, 41.06, 'ZB', 'National data resources survey 2024 (NDA, 29 April 2025)'),
('data_production', 2025, 52.26, 'ZB', 'National data resources survey 2025 (NDA, 29 April 2026)'),
('data_production_growth', 2024, 25.0, '%', 'National data resources survey 2024'),
('data_production_growth', 2025, 27.28, '%', 'National data resources survey 2025'),
('data_production_per_person', 2024, 31.31, 'TB', 'National data resources survey 2024'),
('data_storage', 2025, 2.53, 'ZB', 'National data resources survey 2025'),
('share_of_world_data_production', 2025, 27.44, '%', 'National data resources survey 2025'),
('ai_data_training_and_inference', 2025, 199.48, 'EB', 'National data resources survey 2025'),
('ai_data_inference', 2025, 101.34, 'EB', 'National data resources survey 2025'),
('high_quality_datasets', 2025, 110000, 'datasets (lower bound)', 'National data resources survey 2025'),
('high_quality_datasets_volume', 2025, 908, 'PB (lower bound)', 'National data resources survey 2025'),
('active_data', 2025, 1.67, 'ZB', 'NDA press conference, 11 June 2026'),
('data_market_trading', 2024, 1600, '100 million yuan (lower bound)', 'NDA director at the national data work conference, January 2025'),
('data_exchange_trading', 2024, 300, '100 million yuan (lower bound)', 'NDA director at the national data work conference, January 2025'),
('data_enterprises', 2024, 190000, 'enterprises (lower bound)', 'Xinhua, 28 May 2025'),
('digital_economy', 2024, 59.2, 'trillion yuan', 'CAICT, China digital economy development report 2025 (March 2026)'),
('digital_economy_share_of_gdp', 2023, 42.8, '%', 'CAICT, implied by the 2025 report (+1 point in 2024)'),
('digital_economy_share_of_gdp', 2024, 43.8, '%', 'CAICT, China digital economy development report 2025 (March 2026)'),
('listed_firms_booking_data_assets', 2025, 136, 'A-share companies', 'SAIF data asset tracking report, via Yicai, 24 June 2026'),
('data_assets_booked', 2025, 37.86, '100 million yuan', 'SAIF data asset tracking report, via Yicai, 24 June 2026'),
('data_assets_booked_intangible', 2025, 31.20, '100 million yuan', 'SAIF data asset tracking report, via Yicai, 24 June 2026'),
('data_assets_booked_development', 2025, 6.44, '100 million yuan', 'SAIF data asset tracking report, via Yicai, 24 June 2026'),
('data_assets_booked_inventory', 2025, 0.22, '100 million yuan', 'SAIF data asset tracking report, via Yicai, 24 June 2026'),
('data_industry_scale', 2024, 5.86, 'trillion yuan', 'National Data Development Research Institute, via China Daily, 15 August 2025'),
('data_industry_scale', 2025, 6.78, 'trillion yuan', 'Xinhua, 28 August 2026 (Big Data Expo, Guiyang)'),
('data_industry_growth', 2025, 15.7, '%', 'Xinhua, 28 August 2026 (Big Data Expo, Guiyang)'),
('high_quality_datasets', 2026, 126000, 'datasets (lower bound, August 2026)', 'Xinhua, 28 August 2026 (Big Data Expo, Guiyang)'),
('high_quality_datasets_volume', 2026, 1815, 'PB (lower bound, August 2026)', 'Xinhua, 28 August 2026 (Big Data Expo, Guiyang)'),
('digital_economy_core_share_of_gdp', 2025, 10.5, '%', '15th Five-Year Plan outline (March 2026), main indicators')]
ind = pd.DataFrame(I, columns=['indicator', 'year', 'value', 'unit', 'source'])
ind.to_parquet(f'{OUT}/indicators.parquet', index=False)
# ------------------------------------------------------------------ the "Data Element ×" competition
K = [(2024, 'Beijing', 19000, 100000, None, 12, 119, 'People\'s Daily, 28 October 2024: over 19,000 teams, nearly 100,000 participants'),
(2025, 'Shanghai', 23000, 117000, 893, 14, 183, 'National Data Administration, 28 November 2025'),
(2026, 'Qingdao', 29000, 145000, 330, None, None, 'National Data Administration, 23 October 2026')]
comp = pd.DataFrame(K, columns=['year', 'final_city', 'teams_registered', 'participants', 'projects_in_national_final', 'tracks', 'award_winning_teams', 'source'])
comp['figures_are'] = 'lower bounds ("over"), except 2024 participants ("nearly")'
comp.to_parquet(f'{OUT}/competition.parquet', index=False)
# ------------------------------------------------------------------ places: exchanges and competition finals
C = {'Guiyang': (26.647, 106.630), 'Beijing': (39.904, 116.407), 'Shanghai': (31.230, 121.474), 'Guangzhou': (23.129, 113.264),
'Shenzhen': (22.543, 114.058), 'Qingdao': (36.067, 120.383)}
pl = pol[pol.kind.isin(['Exchange', 'Competition'])][['city', 'kind', 'event', 'year']].copy()
pl['latitude'] = pl.city.map(lambda c: C[c][0]); pl['longitude'] = pl.city.map(lambda c: C[c][1])
pl.to_parquet(f'{OUT}/places.parquet', index=False)
# ------------------------------------------------------------------ road to 2030: official targets (as published, not assessed)
T = [(2026, 'Data market', 'Data industry grows over 20% a year; data trading volume doubles; 300+ typical application scenarios',
'数据产业年均增速超过20%，数据交易规模倍增，打造300个以上典型应用场景', 20.0, '% a year (data industry growth)', '"Data Element ×" three-year action plan', 'NDA and 16 departments', '2024-01-04', True),
(2027, 'AI', 'New-generation intelligent terminals and AI agents used by over 70%', '新一代智能终端、智能体等应用普及率超70%',
70.0, '% penetration', 'Opinions on the "AI+" action', 'State Council', '2025-08-26', True),
(2028, 'Infrastructure', 'Over 100 trusted data spaces built', '建成100个以上可信数据空间',
100.0, 'trusted data spaces', 'Trusted data space action plan (2024 to 2028)', 'NDA', '2024-11-23', True),
(2029, 'Data market', 'Data industry grows over 15% a year (compound, to 2029)', '数据产业规模年均复合增长率超过15%',
15.0, '% a year (compound)', 'Guiding opinions on high-quality development of the data industry', 'NDRC, NDA and other departments', '2024-12-30', True),
(2029, 'Infrastructure', 'Institutions to build and run the national data infrastructure basically in place', '国家数据基础设施建设和运营体制机制基本建立',
None, '', 'Guidelines on building the national data infrastructure', 'NDRC, NDA, MIIT', '2025-01-06', False),
(2030, 'Public data', 'System for developing and using public data resources fully built', '公共数据资源开发利用体系全面建成',
None, '', 'Opinions on accelerating the development and use of public data resources', 'CPC General Office and State Council General Office', '2024-09-21', False),
(2030, 'Digital economy', 'Core digital economy industries at 12.5% of GDP (10.5% in 2025)', '数字经济核心产业增加值占GDP比重达12.5%',
12.5, '% of GDP', '15th Five-Year Plan outline, main indicators', 'National People\'s Congress', '2026-03', True),
(2030, 'AI', 'New-generation intelligent terminals and AI agents used by over 90%', '新一代智能终端、智能体等应用普及率超90%',
90.0, '% penetration', 'Opinions on the "AI+" action', 'State Council', '2025-08-26', True),
(2035, 'AI', 'China fully enters a new stage of intelligent economy and society', '全面步入智能经济和智能社会发展新阶段',
None, '', 'Opinions on the "AI+" action', 'State Council', '2025-08-26', False)]
tg = pd.DataFrame(T, columns=['target_year', 'area', 'target', 'target_zh', 'target_value', 'target_unit', 'document', 'issuer', 'announced', 'quantified'])
tg.to_parquet(f'{OUT}/targets_2030.parquet', index=False)
# ------------------------------------------------------------------ scenarios to 2030: arithmetic paths, not forecasts
S = []
base = 52.26 # data produced in 2025, ZB
for name, g in [('15% a year', .15), ('20% a year', .20), ('2025 pace, 27.3% a year', .2728)]:
for y in range(2025, 2031):
S.append(('data_production', name, y, round(base * (1 + g) ** (y - 2025), 2), 'ZB', 'constant growth from the 2025 survey value'))
for y in range(2024, 2030): # official floor: over 15% a year from 2024 to 2029
S.append(('data_industry_scale', 'target floor, 15% a year from 2024', y, round(5.86 * 1.15 ** (y - 2024), 2), 'trillion yuan', 'compound 15% from the 2024 value'))
for y in range(2024, 2027): # "Data Element ×" ambition: over 20% a year to 2026
S.append(('data_industry_scale', 'plan ambition, 20% a year to 2026', y, round(5.86 * 1.20 ** (y - 2024), 2), 'trillion yuan', 'compound 20% from the 2024 value'))
for y in range(2025, 2031): # straight line to the 15th plan target
S.append(('digital_economy_core_share_of_gdp', 'straight line to the 2030 target', y, round(10.5 + .4 * (y - 2025), 1), '% of GDP', 'linear from 10.5% (2025) to 12.5% (2030)'))
proj = pd.DataFrame(S, columns=['indicator', 'scenario', 'year', 'value', 'unit', 'method'])
proj.to_parquet(f'{OUT}/projections.parquet', index=False)
json.dump(dict(events=len(pol), indicators=len(ind), targets=len(tg), projections=len(proj)), open('out/stats.json', 'w'))
print(pol.kind.value_counts().to_dict(), len(ind))